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Towards the optimal feature selection in high-dimensional Bayesian network classifiers
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
2004 (English)In: SOFT METHODOLOGY AND RANDOM INFORMATION SYSTEMS / [ed] LopezDiaz, M; Gil, MA; Grzegorzewski, P; Hryniewicz, O; Lawry, J, SPRINGER-VERLAG BERLIN , 2004, 613-620 p.Conference paper, Published paper (Refereed)
Abstract [en]

We focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the growing dimension asymptotics. We modify the weighted BN by introducing inclusion-exclusion factors which eliminate the features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy.

Place, publisher, year, edition, pages
SPRINGER-VERLAG BERLIN , 2004. 613-620 p.
Series
ADVANCES IN SOFT COMPUTING, ISSN 1615-3871
National Category
Mathematics
Identifiers
URN: urn:nbn:se:miun:diva-13492ISI: 000224212800076ISBN: 3-540-22264-2 (print)OAI: oai:DiVA.org:miun-13492DiVA: diva2:409502
Conference
2nd International Conference on Soft Methods in Probability and Statistics (SMPS 2004), Sep 02, 2004-Sep 04, 2004
Available from: 2011-04-08 Created: 2011-04-08 Last updated: 2011-04-08Bibliographically approved

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